Update app.py

app.py

@@ -17,14 +17,15 @@ model = AutoModelForSequenceClassification.from_pretrained(name)
 tokenizer = AutoTokenizer.from_pretrained(name, normalization=True)
 
 bench = Benchmark(model, tokenizer)
-text = "hvað er maðurinn eiginlega að pæla ég fatta ekki??????????"
-
-explanations_formality = bench.explain(text, target=0)
-explanations_sentiment = bench.explain(text, target=1)
-explanations_politeness = bench.explain(text, target=2)
-explanations_toxicity = bench.explain(text, target=3)
+#text = "hvað er maðurinn eiginlega að pæla ég fatta ekki??????????"
 
 def get_prediction(text):
+
+    explanations_formality = bench.explain(text, target=0)
+    explanations_sentiment = bench.explain(text, target=1)
+    explanations_politeness = bench.explain(text, target=2)
+    explanations_toxicity = bench.explain(text, target=3)
+
     encoding = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=200)
     encoding = {k: v.to(model.device) for k, v in encoding.items()}
 
@@ -72,23 +73,17 @@ def replace_encoding(tokens):
                  .replace('Ãļ', 'ý')
             for token in tokens[1:-1]]
 
-def predict(user_input_text):
-    explanations_formality = bench.explain(user_input_text, target=0)
-    explanations_sentiment = bench.explain(user_input_text, target=1)
-    explanations_politeness = bench.explain(user_input_text, target=2)
-    explanations_toxicity = bench.explain(user_input_text, target=3)
-
-    prediction_output, keywords, influential_keywords = get_prediction(user_input_text)
-
+def predict(text):
     greeting_pattern = r"^(Halló|Hæ|Sæl|Góðan dag|Kær kveðja|Daginn|Kvöldið|Ágætis|Elsku)"
     
+    prediction_output, keywords, influential_keywords = get_prediction(text)
     greeting_feedback = ""
     
-    modified_input = user_input_text
+    modified_input = text
     for keyword, _ in keywords:
         modified_input = modified_input.replace(keyword, f"<span style='color:green;'>{keyword}</span>")
     
-    if not re.match(greeting_pattern, user_input_text, re.IGNORECASE):
+    if not re.match(greeting_pattern, text, re.IGNORECASE):
         greeting_feedback = "OTHER FEEDBACK:<br>Heilsaðu dóninn þinn<br>"
 
     response = f"INPUT:<br>{modified_input}<br><br>MY PREDICTION:<br>{prediction_output}<br>{influential_keywords}<br>{greeting_feedback}"
@@ -114,11 +109,10 @@ def predict(user_input_text):
         for explanation in explanations:
             if explanation.explainer == 'Partition SHAP':
                 sorted_scores = sorted(enumerate(explanation.scores), key=lambda x: abs(x[1]), reverse=True)[:2]
-                if sorted_scores:  # Check if the list is not empty
-                    tokens = replace_encoding(explanation.tokens)
-                    tokens = [tokens[idx] for idx, _ in sorted_scores]
-                    formatted_output = ' '.join(tokens)
-                    response += f"{formatted_output}<br>"
+                tokens = replace_encoding(explanation.tokens)
+                tokens = [tokens[idx] for idx, _ in sorted_scores]
+                formatted_output = ' '.join(tokens)
+                response += f"{formatted_output}<br>"
 
     return response